Constant Rate Approximate Maximum Margin Algorithms
Constant Rate Approximate Maximum Margin Algorithms
We present a new class of perceptron-like algorithms with margin in which the “effective” learning rate, defined as the ratio of the learning rate to the length of the weight vector, remains constant. We prove that the new algorithms converge in a finite number of steps and show that there exists a limit of the parameters involved in which convergence leads to classification with maximum margin.
online learning, maximum margin classifiers
Tsampouka, Petroula
3b22dca5-f8f4-41a3-b922-8d6def2496bf
Shawe-Taylor, John
b1931d97-fdd0-4bc1-89bc-ec01648e928b
2006
Tsampouka, Petroula
3b22dca5-f8f4-41a3-b922-8d6def2496bf
Shawe-Taylor, John
b1931d97-fdd0-4bc1-89bc-ec01648e928b
Tsampouka, Petroula and Shawe-Taylor, John
(2006)
Constant Rate Approximate Maximum Margin Algorithms
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Monograph
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Abstract
We present a new class of perceptron-like algorithms with margin in which the “effective” learning rate, defined as the ratio of the learning rate to the length of the weight vector, remains constant. We prove that the new algorithms converge in a finite number of steps and show that there exists a limit of the parameters involved in which convergence leads to classification with maximum margin.
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constant_rate.pdf
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Published date: 2006
Keywords:
online learning, maximum margin classifiers
Organisations:
Electronics & Computer Science
Identifiers
Local EPrints ID: 261832
URI: http://eprints.soton.ac.uk/id/eprint/261832
PURE UUID: c021d6af-2445-4046-b41b-c300429a220d
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Date deposited: 26 Jan 2006
Last modified: 14 Mar 2024 06:59
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Contributors
Author:
Petroula Tsampouka
Author:
John Shawe-Taylor
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